Prediction of Global Ionospheric TEC Based on Deep Learning

Zhou, Chen; Liao, Wenti; Li, Haimeng; Deng, Xiaohua; Hong, Sheng

doi:10.1029/2021sw002854

Cited by 57 publications

(58 citation statements)

References 19 publications

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“…Moreover, Chen et al. (2022) used four different LSTM‐based algorithms (single‐step self‐prediction model, single‐step auxiliary prediction model, multi‐step self‐prediction model, and multi‐step auxiliary prediction [MSAP] model) to predict global IGS‐TEC maps. They found that the MSAP model can effectively alleviate the increasing error with prediction time; the MSAP model can better predict the global IGS‐TEC in the next 6 days with RMSE of 3.511 TECU, which is much smaller than 5.593 TECU of the IRI‐2016 model.…”

Section: Discussionmentioning

confidence: 99%

An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method

Shi

Zhang

et al. 2022

Space Weather

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The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short‐term memory (bi‐LSTM) method and observations from 257 ground‐based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi‐LSTM‐based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. The bi‐LSTM‐based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi‐LSTM‐based model. Moreover, the TEC maps resulting from the bi‐LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation.

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Section: Discussionmentioning

confidence: 99%

An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method

Shi

Zhang

et al. 2022

Space Weather

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“…The performance of the modified U-Net model was also compared with that of other state-of-the-art ML models. Chen et al (2022) used long short-term memory (LSTM) network-based algorithms to predict the GIMs between 2011 and 2019. They achieved an RMSE of 3.03, 3.18, and 3.51 TECU for 1-day, 2-day, and 6-day predictions, respectively.…”

Section: Comparison With Other Modelsmentioning

confidence: 99%

“…To the best of our knowledge, all global TEC forecast models mentioned above were trained with IGS GIMs or GIMs from a specific IAAC (Cesaroni et al, 2020;Chen et al, 2022;Lee et al, 2021;L. Liu et al, , 2022J.…”

mentioning

confidence: 99%

Forecast Global Ionospheric TEC: Apply Modified U‐Net on VISTA TEC Data Set

et al. 2023

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This work developed a modified U‐Net model (a convolutional network architecture) to predict global Total Electron Content (TEC) maps. The input includes the current global TEC map, the current F10.7, the time history of the Interplanetary Magnetic Field Bz and SYM‐H in the previous 4 days, the Hour of Day, and the Day of Year. The output is the global TEC map several hours or several days ahead. The modified U‐Net was trained and validated on a brand new TEC database, the VISTA (Video Imputation with SoftImpute, Temporal smoothing and Auxiliary data) TEC database. The VISTA TEC maps can reveal important large‐scale TEC structures and preserve mesoscale structures simultaneously. Taking advantage of the new neural network and the new database, our model achieves an root of the mean squared error from 1.2 TECU to 2.4 TECU as the prediction horizon increases from 1 hr to 7 days. In addition, the model could reveal multiscale structures in the predicted TEC maps.

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“…Chen et al. (2022) utilized a prediction model of global IGS (International Global Navigation Satellite System Service)‐TEC (Total Electron Content [TEC]) maps that are established based on testing several different LSTM network based algorithms to predict ionospheric TEC.…”

Section: Introductionmentioning

confidence: 99%